DBMS > AnzoGraph DB vs. Bangdb vs. Google Cloud Datastore vs. Kinetica vs. searchxml

System Properties Comparison AnzoGraph DB vs. Bangdb vs. Google Cloud Datastore vs. Kinetica vs. searchxml

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Converged and high performance database for device data, events, time series, document and graph

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Fully vectorized database across both GPUs and CPUs

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

cambridgesemantics.com/anzograph

bangdb.com

cloud.google.com/datastore

www.kinetica.com

www.searchxml.net/category/products

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.bangdb.com

cloud.google.com/datastore/docs

docs.kinetica.com

www.searchxml.net/support/handouts

Developer

Cambridge Semantics

Sachin Sinha, BangDB

Google

Kinetica

informationpartners gmbh

Initial release

2018

2012

2008

2012

2015

Current release

2.3, January 2021

BangDB 2.0, October 2021

7.1, August 2021

1.0

License

Commercial or Open Source

commercial

free trial version available

Open Source

BSD 3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

C++

Server operating systems

Linux

hosted

Linux

Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes, details here

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL like support with command line tool

SQL-like query language (GQL)

SQL-like DML and DDL statements

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

C++
Java
Python

C
C#
C++
Java
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions and aggregates

using Google App Engine

user defined functions

yes

on the application server

Triggers

yes, Notifications (with Streaming only)

Callbacks using the Google Apps Engine

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication using Paxos

Source-replica replication

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Tunable consistency, set CAP knob accordingly

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

GPU vRAM or System RAM

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles on table level

Domain, group and role-based access control at the document level and for application services

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